Analyzing Cell Phone Location Data for Urban Travel Current Methods, Limitations, and Opportunities
Analyzing Cell Phone Location Data for Urban Travel Current Methods, Limitations, and Opportunities
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DOI：
10.3141/2526-14
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发表时间：
2015-01-01
影响因子：
1.7
通讯作者：
Gonzalez, Marta C.
中科院分区：
文献类型：
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作者：
Colak, Serdar;Alexander, Lauren P.;Gonzalez, Marta C.
如今，旅行者使用的技术能够以低成本生成海量数据。这些数据可以补充区域旅行需求模型的大部分输出。新的分析工具可能会改变数据和建模在旅行需求评估中的使用方式。最近的工作表明，由旅行数据提供商开发的处理始发目的地旅行如何支持旅行分析。关于如何处理来自电信提供商的原始数据以支持这种分析，或者在多大程度上可以处理原始数据以提取出行行为的报道要少得多。本文讨论了如何处理手机数据以形成一个四步运输模型，重点讨论了此类数据的局限性和机会。图示的数据处理方法仅使用电话数据和人口密度来生成两个大都市地区的旅行矩阵：马萨诸塞州波士顿和巴西里约热内卢。详细说明了如何根据频率和一天中的时间将区域标记为以家庭和工作为基础。通过使用连续停留的标签(家庭、工作或其他)，人们可以为旅行分配目的，例如在家工作。由此产生的出行对根据人口普查数据扩展到总人口。显示了与波士顿当地调查报告的现有信息和里约热内卢现有的始发地目的地矩阵相比较的结果。结果详细说明了一种使用被动生成的蜂窝数据作为交通规划的低成本选项的方法。
Travelers today use technology that generates vast amounts of data at low cost. These data could supplement most outputs of regional travel demand models. New analysis tools could change how data and modeling are used in the assessment of travel demand. Recent work has shown how processed origin destination trips, as developed by trip data providers, support travel analysis. Much less has been reported on how raw data from telecommunication providers can be processed to support such an analysis or to what extent the raw data can be treated to extract travel behavior. This paper discusses how cell phone data can be processed to inform a four-step transportation model, with a focus on the limitations and opportunities of such data. The illustrated data treatment approach uses only phone data and population density to generate trip matrices in two metropolitan areas: Boston, Massachusetts, and Rio de Janeiro, Brazil. How to label zones as home- and work-based according to frequency and time of day is detailed. By using the labels (home, work, or other) of consecutive stays, one can assign purposes to trips such as home-based work. The resulting trip pairs are expanded for the total population from census data. Comparable results with existing information reported in local surveys in Boston and existing origin destination matrices in Rio de Janeiro are shown. The results detail a method for use of passively generated cellular data as a low-cost option for transportation planning.